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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "\n",
    "def SMA(array, n):\n",
    "    \"\"\"Simple moving average\"\"\"\n",
    "    return pd.Series(array).rolling(n).mean()\n",
    "\n",
    "def RSI(array, n):\n",
    "    \"\"\"Relative strength index\"\"\"\n",
    "    # Approximate; good enough\n",
    "    gain = pd.Series(array).diff()\n",
    "    loss = gain.copy()\n",
    "    gain[gain < 0] = 0\n",
    "    loss[loss > 0] = 0\n",
    "    rs = gain.ewm(n).mean() / loss.abs().ewm(n).mean()\n",
    "    return 100 - 100 / (1 + rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Buy the position when:\n",
    "\n",
    " * weekly RSI(30)  ≥  daily RSI(30)  >  70\n",
    " * Close  >  MA(10)  >  MA(20)  >  MA(50)  >  MA(100)\n",
    "\n",
    "### Close the position when:\n",
    " * Daily close is more than 2% below MA(10)\n",
    " * 8% fixed stop loss is hit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtesting import Strategy, Backtest\n",
    "from backtesting.lib import resample_apply\n",
    "\n",
    "\n",
    "class System(Strategy):\n",
    "    d_rsi = 30  # Daily RSI lookback periods\n",
    "    w_rsi = 30  # Weekly\n",
    "    level = 70\n",
    "    \n",
    "    def init(self):\n",
    "        # Compute moving averages the strategy demands\n",
    "        self.ma10 = self.I(SMA, self.data.Close, 10)\n",
    "        self.ma20 = self.I(SMA, self.data.Close, 20)\n",
    "        self.ma50 = self.I(SMA, self.data.Close, 50)\n",
    "        self.ma100 = self.I(SMA, self.data.Close, 100)\n",
    "        \n",
    "        # Compute daily RSI(30)\n",
    "        self.daily_rsi = self.I(RSI, self.data.Close, self.d_rsi)\n",
    "        \n",
    "        # To construct weekly RSI, we can use `resample_apply()`\n",
    "        # helper function from the library\n",
    "        self.weekly_rsi = resample_apply(\n",
    "            'W-FRI', RSI, self.data.Close, self.w_rsi)\n",
    "        \n",
    "        \n",
    "    def next(self):\n",
    "        price = self.data.Close[-1]\n",
    "        \n",
    "        # If we don't already have a position, and\n",
    "        # if all conditions are satisfied, enter long.\n",
    "        if (not self.position and\n",
    "            self.daily_rsi[-1] > self.level and\n",
    "            self.weekly_rsi[-1] > self.level and\n",
    "            self.weekly_rsi[-1] > self.daily_rsi[-1] and\n",
    "            self.ma10[-1] > self.ma20[-1] > self.ma50[-1] > self.ma100[-1] and\n",
    "            price > self.ma10[-1]):\n",
    "            \n",
    "            # Buy at market price on next open, but do\n",
    "            # set 8% fixed stop loss.\n",
    "            self.buy(sl=.92 * price)\n",
    "        \n",
    "        # If the price closes 2% or more below 10-day MA\n",
    "        # close the position, if any.\n",
    "        elif price < .98 * self.ma10[-1]:\n",
    "            self.position.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "GOOGLE=yf.download(\"GOOG\",start=\"2018-01-01\", end=\"2020-06-30\",auto_adjust = True)\n",
    "\n",
    "from backtesting import Backtest\n",
    "\n",
    "backtest = Backtest(GOOGLE, System, commission=.002)\n",
    "stats= backtest.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "text": [
      "\r",
      "CPU times: user 202 ms, sys: 50.7 ms, total: 252 ms\n",
      "Wall time: 6.44 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Start                     2018-01-02 00:00:00\n",
       "End                       2020-06-29 00:00:00\n",
       "Duration                    909 days 00:00:00\n",
       "Exposure Time [%]                     2.71132\n",
       "Equity Final [$]                      10167.1\n",
       "Equity Peak [$]                         10315\n",
       "Return [%]                            1.67137\n",
       "Buy & Hold Return [%]                 30.9831\n",
       "Max. Drawdown [%]                    -3.06834\n",
       "Avg. Drawdown [%]                    -1.27755\n",
       "Max. Drawdown Duration      259 days 00:00:00\n",
       "Avg. Drawdown Duration      140 days 00:00:00\n",
       "# Trades                                    2\n",
       "Win Rate [%]                               50\n",
       "Best Trade [%]                        2.20809\n",
       "Worst Trade [%]                     -0.235516\n",
       "Avg. Trade [%]                       0.978895\n",
       "Max. Trade Duration          24 days 00:00:00\n",
       "Avg. Trade Duration          12 days 00:00:00\n",
       "Profit Factor                         9.37555\n",
       "Expectancy [%]                         1.2218\n",
       "SQN                                  0.788829\n",
       "Sharpe Ratio                         0.566527\n",
       "Sortino Ratio                             NaN\n",
       "Calmar Ratio                         0.319031\n",
       "_strategy                 System(d_rsi=20,...\n",
       "_equity_curve                             ...\n",
       "_trades                      Size  EntryBa...\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "backtest.optimize(d_rsi=range(10, 35, 5),\n",
    "                  w_rsi=range(10, 35, 5),\n",
    "                  level=range(30, 80, 10))"
   ]
  },
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    "### Result:\n",
    "##### While the strategy doesn't perform as well as simple buy & hold, it does so with significantly lower exposure (time in market).\n",
    "##### In conclusion, to test strategies on multiple time frames, you need to pass in OHLC data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens."
   ]
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  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>EntryBar</th>\n",
       "      <th>ExitBar</th>\n",
       "      <th>EntryPrice</th>\n",
       "      <th>ExitPrice</th>\n",
       "      <th>PnL</th>\n",
       "      <th>ReturnPct</th>\n",
       "      <th>EntryTime</th>\n",
       "      <th>ExitTime</th>\n",
       "      <th>Duration</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>200</td>\n",
       "      <td>207</td>\n",
       "      <td>282.95478</td>\n",
       "      <td>275.00</td>\n",
       "      <td>-278.41730</td>\n",
       "      <td>-0.028113</td>\n",
       "      <td>2005-06-06</td>\n",
       "      <td>2005-06-15</td>\n",
       "      <td>9 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>215</td>\n",
       "      <td>235</td>\n",
       "      <td>299.49780</td>\n",
       "      <td>295.01</td>\n",
       "      <td>-143.60960</td>\n",
       "      <td>-0.014984</td>\n",
       "      <td>2005-06-27</td>\n",
       "      <td>2005-07-26</td>\n",
       "      <td>29 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>304</td>\n",
       "      <td>328</td>\n",
       "      <td>372.60372</td>\n",
       "      <td>408.70</td>\n",
       "      <td>902.40700</td>\n",
       "      <td>0.096876</td>\n",
       "      <td>2005-11-01</td>\n",
       "      <td>2005-12-06</td>\n",
       "      <td>35 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>352</td>\n",
       "      <td>357</td>\n",
       "      <td>472.21254</td>\n",
       "      <td>451.17</td>\n",
       "      <td>-462.93588</td>\n",
       "      <td>-0.044562</td>\n",
       "      <td>2006-01-11</td>\n",
       "      <td>2006-01-19</td>\n",
       "      <td>8 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Size  EntryBar  ExitBar  EntryPrice  ExitPrice        PnL  ReturnPct  \\\n",
       "0    35       200      207   282.95478     275.00 -278.41730  -0.028113   \n",
       "1    32       215      235   299.49780     295.01 -143.60960  -0.014984   \n",
       "2    25       304      328   372.60372     408.70  902.40700   0.096876   \n",
       "3    22       352      357   472.21254     451.17 -462.93588  -0.044562   \n",
       "\n",
       "   EntryTime   ExitTime Duration  \n",
       "0 2005-06-06 2005-06-15   9 days  \n",
       "1 2005-06-27 2005-07-26  29 days  \n",
       "2 2005-11-01 2005-12-06  35 days  \n",
       "3 2006-01-11 2006-01-19   8 days  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the trades that would executed with this strategy.\n",
    "stats['_trades']"
   ]
  }
 ],
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